The attentive reconstruction of objects facilitates robust object recognition
Table 1
Model comparison results using MNIST-C and MNIST-C-shape datasets.
Recognition accuracy (means and standard deviations from 5 trained models, hereafter referred to as model “runs”) from ORA and two CNN baselines, both of which were trained using identical CNN encoders (one a 2-layer CNN and the other a Resnet-18), and a CapsNet model following the implementation in [51].